The Impact of Trades on Daily Volatility

Abstract

This article proposes a trading-based explanation for the asymmetric effect in daily volatility of individual stock returns. Previous studies propose two major hypotheses for this phenomenon: leverage effect and time-varying expected returns. However, leverage has no impact on asymmetric volatility at the daily frequency and, moreover, we observe asymmetric volatility for stocks with no leverage. Also, expected returns may vary with the business cycle, that is, at a lower than daily frequency. Trading activity of contrarian and herding investors has a robust effect on the relationship between daily volatility and lagged return. Consistent with the predictions of the rational expectation models, the non-informational liquidity-driven (herding) trades increase volatility following stock price declines, and the informed (contrarian) trades reduce volatility following stock price increases. The results are robust to different measures of volatility and trading activity. (JEL C30, G11, G12) Stock return volatility is fundamental to finance. Contingent claims pricing, risk management, asset allocation, and market efficiency tests use volatility as a basic building block. Consequently, volatility has been widely studied along several dimensions. Volatility clustering in financial data has been well documented and extensively modeled by the autoregressive conditional heteroskedasticity (ARCH) of Engle (1982) and the generalized ARCH extension of Bollerslev (1986). 1 Shiller (1981), LeRoy and Porter (1981), Roll (1984, 1988), French and Roll (1986), and Cutler, Poterba, and Summers (1989) have all documented a significant amount of volatility that cannot be explained by changes in fundamentals and, thus, may be attributable to mispricing. Schwert (1989) has studied the variation in stock market volatility over time and has analyzed the